Most “sample class” predicting systems are currently implemented using a single node approach where clustering algorithms have the luxury of evaluating an entire data set together. Each data point can be evaluated in conjunction with other data points. However, the single node approach presents scalability issues, such as cost (more powerful machines are more expensive) and performance (clustering a big data set on a single node may take a long time).
In view of the foregoing, it may be understood that there may be significant problems and/or shortcoming with current predicting systems.